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From Many Models, One: Macroeconomic Forecasting with Reservoir Ensembles

Author

Listed:
  • Giovanni Ballarin
  • Lyudmila Grigoryeva
  • Yui Ching Li

Abstract

Model combination is a powerful approach for achieving superior performance compared to selecting a single model. We study both theoretically and empirically the effectiveness of ensembles of Multi-Frequency Echo State Networks (MFESNs), which have been shown to achieve state-of-the-art macroeconomic time series forecasting results (Ballarin et al., 2024a). The Hedge and Follow-the-Leader schemes are discussed, and their online learning guarantees are extended to settings with dependent data. In empirical applications, the proposed Ensemble Echo State Networks demonstrate significantly improved predictive performance relative to individual MFESN models.

Suggested Citation

  • Giovanni Ballarin & Lyudmila Grigoryeva & Yui Ching Li, 2025. "From Many Models, One: Macroeconomic Forecasting with Reservoir Ensembles," Papers 2512.13642, arXiv.org, revised Jan 2026.
    • Handle: RePEc:arx:papers:2512.13642
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    References listed on IDEAS

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